Alternative specification, estimation and identification of vector autoregressions

Identification and estimation of non-Gaussian structural vector autoregressions

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to (essentially) uni...

On the Identification of Structural Vector Autoregressions

F ollowing seminal work by Sims (1980a, 1980b), the economics profession has become increasingly concerned with studying sources of economic fluctuations. Sims’s use of vector autoregressions (VARs) made it possible to address both the relative importance and the dynamic effect of various shocks on macroeconomic variables. This type of empirical analysis has had at least two important consequen...

Modeling and Estimation of High-dimensional Vector Autoregressions

Modeling and Estimation of High-dimensional Vector Autoregressions by Sumanta Basu Chair: George Michailidis Vector Autoregression (VAR) represents a popular class of time series models in applied macroeconomics and finance, widely used for structural analysis and simultaneous forecasting of a number of temporally observed variables. Over the years it has gained popularity in the fields of cont...

Anticipated Fiscal Adjustment and Identification of Vector Autoregressions

Due to the complex nature of fiscal policy decisions and implementations, fiscal vector autoregressive models have suffered from an underidentification problem that has caused substantial debate about measuring the efficacy of fiscal stimulus. To help resolve this issue this paper incorporates additional information regarding how private agents’ anticipation of fiscal adjustment in the short an...

Vector Autoregressions and Cointegration*